Typical commercialized petroleum reservoir visualization software helps petroleum and reservoir engineers and geoscientists see the results from static or dynamic simulations and visually compare iterative “what if” scenarios. Many reservoir models are often described as a disconnected curvilinear grid volume, also called a “3D grid,” where each grid cell has clearly defined hexahedronal geometry. The software shows different views of the reservoir with particular attributes (e.g. gas saturation) of the reservoir. The edges, top, and bottom of the reservoir can be seen by rotating the view. Visualization software typically allows the representation of any simulation attribute, instant switching between attributes, and the ability to set data thresholds with unique displays of cells that are restricted to specified data ranges. A visualization model may include a single layer, or multi-layer views wherein cells are stripped away to reveal the inside of the model. They can also be constructed to show a full display of corner points and local refinement for grid volumes.
A 3D reservoir model may be presented as hexahedral grid cells, which can be topologically structured or unstructured and geometrically regular or irregular. Curvilinear grid volumes, which are topologically structured and geometrically irregular, are more typical in reservoirs and are therefore, of particular interest. A 3D grid may be defined as: cell=f(I, J, K)=(v1, v2 . . . v8, a1, a2 . . . an); where v1, v2 . . . and v8 are eight vertices for the cell and a1, a2 . . . and an are attributes. 3D grids are I layers thick, J cells wide, K cells deep, which contain cells with coordinates (I, J, K) referred to as grid coordinates. Grid Coordinates (I, J, K) are typically used in an index domain, while Cartesian (world) coordinates (x, y, z) are typically used in a sampling domain.
Research for unstructured volume visualization includes the widely used Projected Tetrahedral technique. Many other extended and enhanced algorithms have also been published. Another algorithm used for visualizing geoscience data is incremental slicing, which was first introduced by Yagel, et al. in Hardware Assisted Volume Rendering of Unstructured Grids by Incremental Slicing, IEEE Visualization, 1996, pp. 55-62. The basic idea behind this algorithm is to slice the whole grid volume along the viewing direction and render the slices from back to front. For surface volume rendering, the well known Marching Cubes algorithm can be used for rendering both regular and irregular grid cells. The challenge of scientific visualization, however, lies in determining which algorithm best fits a particular domain and task. In this respect, selecting a visualization software solution is largely dependent on the particular data domain because most visualization software solutions do not provide a flexible framework for using a preferred, external, visualization algorithm as-is or will require a substantial revision of the algorithm to fit the visualization software framework.